Eric Vishria - A Decade of Lessons Investing in Software & Hardware - [Invest Like the Best, EP.486]

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0:59 FelixByrogo is a personal finance agent that turns a single prompt into finished client ready work using your firm's own templates, context, and standards. Send Felix an email like take these comments and turn them for me or update my tracker with the context of these emails, and Felix sends back finished PowerPoint decks, Excel models, and sourced research. Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at rogo.ai slash feelix. Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money.

1:39 If you enjoy these conversations and want to go deeper. Check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts at Colossus.com. Mm-hmm. Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc.

2:18 Um I love asking you and all your partners this every time we hang out, which is okay, you've got these singular investments, you don't do that many of investments each per year. And then the ones that go on to work so far, Sierra and Fireworks certainly are You get to learn so much about the world through the lens of the company. So I'd actually love to do both of those, maybe starting with fireworks.

2:40 What do you know or what have you learned about the world and how it's reordering itself? based on watching the world through the lens of fireworks that would maybe be surprising or interesting. One really interesting thing. Is These models are big.

2:53 These are two trillion, three trillion, four trillion parameter models. It turns out running those models Is damn hard. And running them efficiently.

3:03 is like super hard. The way to see this, and everybody can see this. Which is Everybody From AWS to Azure to GCP.

3:14 to the neo clouds, to the fireworks based on together of the world. Like all of them. They all run. The stock. Open source models. that are available in their developer pools and everything else. And that's what it is.

3:27 The performance difference. For A fireworks versus A cloud provider. is like five X.

3:35 And that is just The speed performance. Then you add on top of that. throughput which is not visible externally. It's only visible if you know the economics of these businesses. And you're like

3:46 Wait a minute. This is the same open source model. With The same NVIDIA hardware. And there's a five X performance difference and a multiple X throughput difference. And the way to just simply understand that is

3:59 These companies are paying the margins of the cloud providers and running on top and making money. How can that be? My big takeaway on it was, wow, this stuff is actually really hard to run. It's just really hard to run. And there's a lot of expertise involved in doing that. And it's this very specific expertise that exists.

4:17 And it Is the kind of thing that when you look at it as an investor from the outside, and we should talk about the early days of AWS, but When you look at it from the outside. It's like this is a commodity. This is just just like pass through resell scale game. Yeah. Scale game, like whatever. And you're like, oh, wait a minute. No, it turns out it isn't.

4:33 It isn't at all. Do you think that's just a moment in time thing? And I'd love to just hear you riff on like cloud. You watch cloud very carefully and closely. You know a lot about it. And the adoption curve there versus how people use these things and the nature of those two businesses in comparison. It's tempting to say. There will be one or two scale winners like there typically have been in a commodity market.

4:51 Total or cost to serve is everything and scale drives cost to serve down and like that's the whole story. I think the AWS example's so good. Okay, so Two thousand six. You have S three and E C two watch, right? Their compute platform and their storage platform is the first two AWS offerings in two thousand six. So you can kinda start talking about it in late two thousand six or whatever. Two thousand six, two thousand seven. I think the two thousand seven annual letter basis talks a lot about AWS and why it's important, why it's interesting and everything else and and the investor reaction.

5:19 It's just Not good. I think if you put thirty of the smartest investors at that time in a room And ask them what's the probability? That this AWS business is a good business with durable long term margins and like super interesting and everything not commodity. I think you would have gone zero for thirty.

5:38 With really smart people that you and I know who were out at that time in O seven. Fast forward from oh seven to twenty fourteen. I joined the venture business in twenty fourteen and a really common narrative in twenty fourteen. Was Oh my God, AWS is gonna eat everything. There's no enterprise opportunity left. It's gonna eat databases and infrastructure, but it's gonna eat the apps too, and they're gonna offer it the cheapest and best. And

6:01 We all have these like amazing SaaS and software businesses. that we were involved with or investors and part of the reason people loved them was they were annuities and they ran at eighty five percent gross margins and everything else. And so it was just like, oh my God, AWS. Amazon can offer the ex eight percent gross margin and like they're just gonna crush the market my opportunity. Yeah, your margin my opportunity, like the whole thing the whole narrative. And think about from like twenty fourteen to now.

6:25 In enterprise. Of course you have Snowflake direct competitor. Two Amazon redshift. ran on Amazon.

6:35 You're out Amazoning Amazon on Amazon. But it's not just Snowflake. You had Confluent and Elastic and Mongo. Data bricks. All of these companies. Amazing. That's the infrastructure layer. Then you have the whole app layer.

6:48 In the app layer. Think about offerings that they offered at the beginning. DataDoc Hundred billion dollar company. Today. They had a competitive offer.

6:56 And they did it. And of course there was tons and tons of roadkill. There was tons of roadkill. They did one over a bunch of stuff. But even then, in twenty fourteen, the thesis was The view that AWS was gonna eat everything was massively wrong, not because of all the examples I just mentioned.

7:12 It was massively wrong because Azure and GCP were irrelevant then. And fast forward to twenty twenty six and they're unbelievable businesses. Is AWS the biggest? I think it's like a 40, 30, 20 split. You ended up with an oligopoly of that. And even Outside of those big three.

7:28 You have Cloud Flare. Which is. A cloud provider in a different sort, which is another hundred billion dollar company. So you have these smaller players that emerged as hundred billion dollar companies outside of it. So what's the takeaway? The takeaway to me

7:42 Is oh there's just a bunch of zero sum thinking and not realizing like how Big. What if it all works. What if it all works. It all worked. And of course getting the relative winner right matters and there was roadkill and so it's all those things still matter. So I'm not saying Spray and pray. I'm not saying that at all. But I'm just saying

7:59 The market was so big. That One vendor could not scale and consume it all. And they just couldn't consume the whole industry.

8:08 It's different now and like all these things. But just that whole notion right now with what you and I are seeing in AI. Sure feels like it rhymes. Anthropic's gonna do everything. Wait, really? Is that really right? To me, that view that it's just like, oh, this one company's gonna eat it all doesn't hold. And I would tell you that scaling while cloud scaled very quickly.

8:29 Cloud did not scale anywhere close to as quick as what's happening right now. for that company. to actually scale and deliver it. Scaling in this case requires A

8:40 Ton of infrastructure build up. From Energy power shell. Chips. Memory

8:47 Obviously the algorithms and everything else on top. It feels to me like we're gonna end up with an oligopoly. I really believe they will be these like hundred billion dollar crazy Smaller. Winners.

8:59 Just feels like the same thing is kinda happening. Can you talk about it also from the demand side and compare it to how you watched cloud get adopted in enterprise versus how you're seeing AI get adopted now. So if you go back to like two thousand ten, two thousand eleven. Now you're like three, four years into AWS being offering. Enterprises were

9:19 Super skeptical. traditional enterprise, blue chip enterprise. You had digital natives out here, you had new companies forming that were Using the cloud. I think kind of famously.

9:28 Snapchat. was built on G C P and I think at one point in this era. maybe twenty twelve, twenty thirteen, twenty fourteen. Snapchat was like forty percent of GCP. Those kinds of things were happening.

9:40 That's like cursor being thirty percent of all strength. Hundred percent. Same exact thing happened. Yeah. Same exact thing happened. Enterprises were very sceptical, I think, of cloud. Until by twenty fourteen, twenty fifteen, twenty sixteen it's like, Oh yeah, yeah, yeah. Then I think the big banks and financial services and insurance companies and more conservative blue chip enterprise were like, Oh, wait a minute, this is actually different and we're gonna have to pay attention.

10:02 it became an issue in recruiting for them because they couldn't get the best developers'cause best developers wanted to work. on the easiest platforms and all these kinds of things happen. So the difference now feels profound to me. Because

10:14 While It is a hundred percent the case. That blue chip enterprise. AI is not well absorbed and well adopted and not the same thing as going to cursor and walking the halls of cursor versus

10:26 Walking the halls of A big New York financial services firm. In terms of their AI use. They want it.

10:33 They wanna figure it out. They're running experiments. They are spending against it. They're trying to figure it out, talking about it. They're not being dismissive about it. And I think They view it probably as More of an opportunity. than they did the cloud in terms of the potential impact on their business.

10:49 I think they probably view it more as a threat. Then they did. The cloud. Maybe they just also learn lessons from the cloud in terms of what's possible here.

10:57 I feel That They will continue to try to figure out and absorb it. Having said all that

11:05 I do think one of the more interesting things If you go from Silicon Valley out into the world. And talk to these companies, these enterprise companies. And you realize what the pace of adoption is and what the barriers to adoption are and everything else.

11:20 There's a ton of opportunity. to help the enterprises get their tell the companies I work on is hey, let's be their AI Sherpa. If we're in that position

11:31 to be their AI Sherpa where we're crossing both worlds, that's very valuable. And I think that'll continue to work. What is Sierra teaching you about the adoption of this stuff. It's really interesting in contrast to Fireworks, where Fireworks is the sort of infrastructure provider. Sierra is feeling its way through what are really cool things that we can do for end consumers, enable companies to do for end consumers, starting with customer service, but I know with Horizon now going beyond that. Again, same question is for fireworks. What do you know that the world doesn't fully appreciate because of how you've seen that? My partner Peter

12:03 And Brett. I think this is their third company working together. So it's like a twenty year relationship, which is just like an amazing and great place to start. And the team is

12:15 They're technologists. But they actually have like lived in enterprise world for a long time and really understand it and everything else. And I think Actually he's personified this whole idea of hey, let's be Their AI Sherbu. Let's start with customer service. We're very unmetable and like be there air short, but now we have these long running agents with horizon that can do

12:33 More and more stuff. And one of the things that I think is most interesting about this to me is Yes, it's an application company. On the surface. But They're doing real AI work and they're experimenting with models and they're building agents and they're very close to the metal of the models and the capability and the harnesses and they're understanding the jagged edge.

12:53 Of AI capabilities. Which is very different than the human smooth arc that we understand intuitively. They understand that jagged edge of capability.

13:04 And they build around it. You saw the evolution of cursor from the IDE to like Tab Auto Complete. to agentic work. Over and over and over again. They were Obsoleting their work from

13:16 Six months ago. And it's what Brett Taylor calls like sand castles. We used to be building castles, now we're building sand castles that get washed away. And that's if you don't think it's more that way. Yeah, you have to embrace it. Because if you're an artisan and you're like, Hey, I built these perfect foundation of castle and it's just bricks and it's like perfect and I really care about this and it's gonna be here for a hundred years. You're just not gonna make it.

13:37 So as you get emergent new properties in the models, which is yeah, every new every four weeks, you get new capabilities. They understand that jagged edge. They understand that application of that jagged edge. Or that valley. Two their customer base.

13:52 They're filling in those gaps and translating it. You can't just be superficially applying things. I think it's actually a complete inversion of how product development used to work. to how product development happens now. And product development, you'd also say like, hey, product manager you train, oh product manager should understand the technology, but like really shouldn't be thinking about implementation and shouldn't be specifying implementation and shouldn't be doing this and doing that. And the product manager's job is to really understand the customer and translate that problem.

14:19 to the engineers so that the engineers build the solution like that was traditional product management. Good luck doing that now. That's a horrible way to do it. You can't do it that way. You actually really need to understand the nuances of model capabilities, what they're great at and where they fail. And you need to understand the cost from a problem. And put those together.

14:37 and bridge those gaps to build valuable solutions. I think that's one of the things that Michael and team at Cursor did so well. from the very beginning. So they really understood the jagged capability and built a product that allowed that translation from developer to that jagged capability.

14:53 And kept on. All that sounds like the returns to being technical are going up. Even as models supposedly are taking away technical edge. Well I think there's two things kinda under it. Yeah, a hundred percent. Yeah, and actually I think there's this weird thing where

15:10 There was this whole discussion about the traditional roles of product manager and designer and engineer. Whatever it is. I think what there really are. Or People who understand customer problems. Пилку.

15:22 And people who understand the jagged edge of AI capabilities and are curious about it. Those are the three things. Fear got those three are gonna do. If you're gonna be if you d those three things, you're gonna do great. And it doesn't really matter if you are an engineer or a product manager or designer, but if you have

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16:32 advanta.com slash invest RidgeLine is the first end to end system of record with embedded AI for investment management firms. Running portfolio accounting, reconciliation, reporting, trading, and compliance. on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead's AI features are compared to anything else in investment management software.

16:56 Which is why I believe that firms that come out ahead in the AI era will be the ones running on Ridgelines unified platform. If you're serious about your firm's AI strategy, RidgeLine should be part of that conversation. You can request a demo at ridgeline.ai. When we first did this so many years ago now, it's just crazy. It'd be fun to revisit some of the ideas that we talked about the first time with SAS. But you use this term that I've used ever since, which you call the the competitive frontier.

17:24 Meaning like the things that will determine the winners and the losers. My partner Brie has this great idea that's stuck in my head, which is that everything is a jump ball right now. I'm really curious, in addition to this idea of sand castles versus real castles, what else you're seeing amongst the people that are becoming competitive winners. Are the traits different for winners now, personality wise or business strategy wise or business model wise?

17:46 Versus what you learned in the SAS era. I'm very dismissive of this idea that people are gonna vodco their own shit. And whatever. That isn't the issue at all with SaaS companies. The issue at South Companies is competitive frontier completely shifted.

17:59 And everything they thought they were building against and it would make them win. Or Not what's gonna make'em win. Let's take databases. Databases have been a phenomenal area for software for a long, long time.

18:11 Great margins. Why? You had app developers that would build against these specific database interfaces that existed for that database. you would have more and more data over time migrating An app.

18:23 from one database to another database was a giant project that was like very, very difficult to do. So these were unbelievably sticky businesses. that you could generate a ton of margin and right. Of course you got Oracle and SQL Server and like and a whole slew of smaller players like that did really, really well in databases. Well let's think about that in the context of AI. Now.

18:43 You don't have a developer building against a database interface. You have cloud. Or codex building against the database interface one. Two. The beauty based interfaces is They're very, very well specified.

18:55 Well, it turns out very good very good at things that are very, very well specified. Three. Agents don't get tired of monotonous work of translating one specification to another. So it turns out That Now all of a sudden

19:08 Database migration. Which used to be. the like number one thing you would not do in software. Is like Just model. Yeah.

19:17 Yeah. It's just like yeah. Put some money against it, move it. So what changed? Well what changes is The criteria To be an amazing database company changed.

19:28 It isn't that we don't need databases or that everyone's gonna build their own database or whatever. That isn't what's gonna happen. What's gonna happen is the criteria change. So now you're gonna have a ton more applications that start. Obviously as we're seeing everywhere. People are gonna experiment a lot more'cause it's much cheaper to experiment.

19:43 It's much cheaper to start a new application. So now you need databases that scale from basically zero usage to if it works all the way through. Like that matters a lot more. Your cost. matters a lot more. You wanna be able to spin these things up, spin'em down, tear'em apart.

19:59 over and over again. So the iteration speed goes up and what you need on database. And ultimately I think the cost becomes the arbiter of this. So the cost and then this Zero to infinity scaling. And transportability and everything else around that.

20:14 become like the arbiters of who wins and who doesn't. That's really different than hey, I spec this database for our user thing and I procured a license and I ran it on this kind of hardware and Everything else. There have been elements, of course, of these things over time. But I think that just criteria changed. And I think one of the big messages to these SaaS companies

20:33 A few years ago Was You have a choice. Get to AI. Or be worth three times revenue.

20:40 Well, it's a hard message to hear. You're just like get to AI or three times revenue. Those are your choices. We say three times revenue now because A lot of these public SaaS companies are trading at six times or whatever.

20:50 But keep in mind in twenty one they were going for thirty times. Like everybody. You're like three times wow. I've drawed out. Four X since then. This is a whole multiple compression is a bitch. Yeah. I've grown four X. The multiple has gone down by a factor of six, so I'm worth less, even though I've grown four acts of years and uh gotten to break even all these things. So like that was the first message. But I think that even it became really visceral to me where You were in these meetings and you were like, Hey

21:15 Every single day. That you are hitting Your plan. You were destroying equity value. Think about that.

21:23 Our whole careers we learned. You lay out a plan. You execute against it relentlessly and violently. you hit your plan or you exceed your plan and you keep on building that and that's how you build equity value. That's what the whole management team's learned.

21:37 That's what these CEOs learn pre AI. This is like everything learned. And now you're joining in here just every day you hit that point. You're fucking up. You're fucking up. You're destroying value. And the point of Saying that to them was to set them free.

21:51 Another articulation of this from my friend Ann Lee Skates. It was just The CEOs who were going through this transitory period and had a business that was at hundreds of millions and he thought that you know they're all ready They were working on their business from eight AM to five PM.

22:05 And then try to do A I from five to eight. In the evenings. And what they needed to be doing was the inverse. And it's so hard to do that.

22:14 because of all of the training and all of the muscle memory and all the inertia and everything that we've learned about the hill that we were climbing. Like we're all co climbing in a way. The success model was Set the plan, execute the plan. build value, compound value.

22:29 And it's like oh no. No, no, stop that. You gotta completely invert. This is a very, very long way to get back to your question of what the profile or what the mentality of the winners are right now. But if we look at Brendan from Record or Lynn from Fireworks or or Max or Brett.

22:48 Any of these people. They're so nimble. About what the eval is. What are they optimizing against? They are so nimble on all of it.

22:58 And if you look at every one of those companies the evolution of the business. It's just the business is constantly evolving. And they've done such an excellent job at that. I think that is very, very different than The way I

23:12 What Talk. What's your sense of the disorienting nature of model progress? You're one step removed from that as an investor versus as that technical founder with your hands on the metal. How are you behaving?

23:24 differently than you would have three years ago or something because of the pace. Any time that I'm talking to a founder about a problem in their company or what they're doing or a move they're making or anything else, which is What I spend eighty percent of my day doing.

23:39 I'm very This is how we used to do it. This is what we would typically do. This would be the typical readout on this old school readout of why this candidate is better than this candidate. Let's reevaluate that in the context of today. Let's reevaluate that in the context of an unstable technology substrate.

23:57 Let's reevaluate that. in the context of a business model that's growing this way versus that way. I've really started to question every assumption. And every lesson that I learned before. Which of it translates and which of it doesn't.

24:11 That's a huge difference. I'll give you a really concrete example. Which has been very disruptive inside of these scaling AI companies. So these scaling AI companies

24:22 We've had a lot of leaders come from the prior generation with great experience and everything else come to these scaling AI companies and completely flame out. And you see it across the industry. And so the question is why? These are some of the best leaders. From four or five years ago, they had all the lessons, they learned it all.

24:39 They're excellent. But somehow it's not translating. And there's some impedance mismatch between The AI founders potentially. the needs of the business and what these people are bringing.

24:50 And I saw it really abruptly w with a particular sales leader who we hired, who's like, Hey, we can't hit Any of these things because The way that software sales has been taught forever is a quota capacity model. You have a quarter capacity model, each rep does this.

25:06 In the early days of a company, the quotas are one point two, one point five million. maybe the ISRs are at seven fifty or eight fifty. And then over time it scales up and enterprise gets to two point five million. That's how all these financial models are built. You start with a quarter capacity model, you take a discount on attainment. This is what we can do.

25:22 Boom boom boom. Fundamentally. Without realizing it. Everybody was implementing something.

25:29 Was Based on Pushing demand, not pulling demand. And for so many of these companies They're operating here, these customers.

25:38 And there's a new AI enabled product that comes along and it's just fucking magic. So these companies are selling magic. Well turns out if you're selling magic. And you're the first one there. You're gonna sell a lot more than two million.

25:51 The whole notion of a quarter capacity model and not being working. It's not that it doesn't matter. Matters kind of. But it's a definitely not the first order thing or constraint. So you have these execs come over with us. It's like here's our math and here's the territory assignment and here's what we would do and

26:06 First you do West Coast and then you do East Coast and then you do Central Like all these things. Oh wait, no no, it doesn't work like that at all. One of my big things that I started to realize is last time interviewing these folks and talking to them is just like hey You need to check everything at the door. Check it all. Which is probably good practice anyway, but check all the baggage.

26:23 Check everything that you learn and just learn this from first principles. How's it working? What are really the bottlenecks on delivery? What are the bottlenecks on demand? Because It turns out that In a lot of these companies. You have reps doing ten or twenty.

26:38 Thirty million. I saw fifty recently. It turns out that's different. What is like the best salesperson that you've seen that's doing it in a de novo way doing? Honestly, the best salesperson in any of these companies is a founder. And what they're doing is bridging the jagged edge to what the customer's capability is. And that's it. It sounds so simple, but it's not.

26:55 But that's what they're doing. The market is just so big. Just like we were talking about with cloud. I think the biggest mistake everybody made was they just undersized the market. And it turns out the market's just really, really big. And this market is

27:07 Figure. It feels like right in this moment in time. Actually just this morning, you and I are in this great group chat together where the discussion is. The demand for intelligence. Seems kind of unlimited and it seems like the smarter the thing gets, the more demand there is. Maybe the bottleneck is just capital.

27:21 The world just feels like it needs to take a breath. The RSI concept, if you apply it across technology, doesn't need to breathe. The agents don't get tired. But the world feels kinda like, Oh man, it sure would be nice to have three months just to like Digest this a little bit and for capital to form and evaluate its prospects and the scale is getting so big. Does it feel to you like this can just

27:43 Keep going. Or are we just gonna get tapped out of money that can be invested? In these things when it seems like we could consume like any amount of money to build any amount of stuff and serve any amount of inference. I'm not a macro economist.

27:56 I am worried about energy. If you think about the models as translating compute into intelligence. Quite simply, what do models do that very effectively translate compute into intelligence? What's the demand for intelligence? Well, it seems like a lot. So then it follows that

28:11 We will continue to have more and more demand on compute. I'm saying compute probably not chips, not storage, not whatever. But then like what do we need for compute? We need energy. A lot of energy.

28:23 There's all this topic of distillation and Chinese open source and all these things, but The bigger thing to me is that I think China's bringing on ten times as much energy next year as we are. In the US.

28:36 If Energy is what you need for compute. And is the bottleneck. And there's unlimited demand for intelligence that it stands to reason that if we have a lot less energy

28:47 Then we will have a lot less. Intelligence. Relawless tokens. Yeah. A lot more expensive tokens.

28:53 If we have a lot more expensive tokens than supply demand, you're gonna end up with Lush. And that seems very bad. To me, I think the energy Bottleneck.

29:04 However that is, it'll manifest in twenty different ways. Gas turbines go up and down, natural gas go up and down and Soul or whatever. All these different things, rare earths. That to me is probably more concerning.

29:16 And if I'm Thinking about it from the regulatory perspective or government perspective. And I think the administration is doing some things around this. Fostering investment and development of

29:27 All energy. solar, nuclear, gas, like what up, do it all. We should do it all. And it'll work itself out. This is one of these things where yes. One will be relatively better than the other, and I don't know and I'm not smart enough to predict which one's which, but it'll all work. Speaking of compute, I would love to hear the Cerebra story.

29:43 I haven't heard you tell the full version of this. The reason I'm asking about it is I'm deeply interested in compute. I have big investments in compute. And I'm fascinated by it. It's just like the most magical thing to watch happen. It's mind boggling when you get close to one of these things, what humans have been able to do. on these chips and in these systems.

30:00 I think you invested in twenty sixteen or thereabouts. I think it was your first foray into like extremely difficult hardware type investment. It's so hard. And Now the world is full of opportunities like this. Whereas back then it was a one off.

30:14 Teach me everything you've learned about Hardware investing through Cerebrus. Mostly it's really hard. It's an amazing example to me about The naivete required. The company came in in twenty sixteen, it was five founders and a deck.

30:28 I do not want to go to the pitch. But it's my job. Because I'm like, why are we going to go to hardware investment like this crazy? been ten years since we've made a semi investment. I think basically the team was excellent and then And the kind of first slide was just like GPUs actually suck for deep learning. They just happen to be a hundred times better.

30:45 And CPUs. You have to remember this is pre transformer. OpenAI is just weird research lab at this time. NVIDIA was worth like forty billion, not four trillion. The TPU hadn't been announced.

30:58 No. So this is early. But the whole idea As soon as he said it, I was like, Oh shit, of course. Of course, like why? I had spent the last eighteen months trying to figure out applications of deep learning and looking at the security thing and looking at this medical imaging thing and like all this other stuff.

31:13 thinking like hey, there must be something here that's gonna be really transformed by this stuff. Anyways, we go through this whole journey we end up investing. Which was amazing. We first met on Wednesday, party meeting on Monday, had a bunch of meetings in between. just built a lot of conviction.

31:28 That This was a great swing. And I'll tell you what I understood and I really just understood so little. But basically there are three things that we know how to do to speed up deep learning and hardware. Still till this day.

31:41 Increase the number of course. Increase the communication between cores. Bring the memory closer to the compute. Those are the three things. That's it. Those are only three dimensions that we know. And hardware.

31:51 My articulation of what they said to me Honestly all I understood. Was Let's just take all three of those things to their logical maximum. You have a wafer scale chip.

31:59 At that time, you would have four hundred fifty thousand cores on it. You'd have something like twenty gig of S RAM on the chip. So you never have to go off chip to get to memory. And because they were all in the same wafer. the communication between cores is maximumized. So this is the best you could do. On that process. I think first the first chip was seven anime or something.

32:16 You're like. That's it. That's what we do. And it turns out That In software, if you have that

32:23 logical block diagram of like why it works and everything else, you're kinda eighty percent of the way there. And it's a matter of go to market execution. In hardware. You're like two percent of the way there. There's things like physics. Entire supply chain of vendors

32:38 There's of course TSM C, which everyone knows, but it's not just TSM C. There's thirty other vendors that matter and putting all this stuff together and everything else. I didn't know any of that. Fast forward from twenty sixteen to like twenty nineteen, I think they got their first parts back.

32:52 You go through this like bring up and then it's like bring up Oh yes, we got a partner. And then it's like Yeah, and then you gotta go through like we bring up and there's like fourteen steps. So bring up and everything else. And then by like twenty twenty, we had our like first thing that works. Then it's just this March. of actually getting it to work.

33:12 One of the lessons that I've learned on this stuff is basically you go through all these sims and everything else. In hardware and semis in particular. That basically is your roof line. Best it's ever gonna be is what that is. And then every bit of software and reality and compilers and kernels takes away from that roofline.

33:31 You might start at ten percent of the roof line. Once you bring it up. These guys are grinding for months and years to like get closer and closer and closer to the roofline. It's really different and it's really hard. I'm

33:43 astonishingly bullish if I kinda rewind. Part of the reason we made the investment. Was If you looked at the four prior generations of compute In my lifetime, so you had CPUs.

33:55 You had. Graphics. the networking the mobile. There was a new workload. Each time. So you had multi purpose compute and it loaded to the CPU. You had massive

34:05 Parallelsm. led to the graphics processor. Graphics processor offered massive parallelism, which led to graphics. Then with networking you needed really low latency chips and so they had low latency chips. And then with mobile, you needed really power efficient chips. And in each case. We ended up with a new hundred billion dollar company.

34:20 The first question. going back to twenty sixteen was is AI that big a new workload? Because there've been many, many other attempts. for specialized chips for other things that really just didn't end up mattering. There's some fine outcomes, but like just didn't really end up mattering, et cetera. Right, exactly. And so it's just like well, okay, well you need something that's A really, really big workload. Okay, so that's one.

34:40 We have a lot of conviction on that. And then two. Was the nature of the workload did it introduce a new Constraint or problem. In it. What I learned was basically the AI workload benefited from the parallelism of GPUs massively.

34:55 But GPUs didn't solve the core to core communication, basically the layers of the network problem. You're like, okay, wait a minute. There is a new constraint, which is communications communication bound problem. Then you're like, okay, is AI

35:07 a new giant workload that is going to have specialized chips. Everything that I just said was the entire everything I knew at that time. That's obviously played out. And

35:18 In each prior generation. We got Intel, we got NVIDIA. We got Broad Calavago. We got Qualcomm and ARM when each of these generations and There will be these giant winners, standalone winners. Obviously the TBU itself is a winner, training's a winner.

35:33 You've had Quak and Cerebrus. Actually. It'll keep getting fought out. But I think that wind up being big. And actually I think there's a new sixth one that's coming.

35:41 generation which is And I'm really excited we've made an investment that's unannounced in this, but I think that for the first time In a long time. there's actually room for a new CPU approach. The thing that's happening right now, and you see this reflected in all the semi stocks and everything else, is

35:58 The LMs which are running on accelerators and GPUs. generate code. The code runs on CPUs. And

36:08 Right now it's running on Classic CPUs we've had around forever. But There's a whole bunch of constraints on CPUs that have existed and CPUs have dragged all this baggage forward. that you might not need to anymore.

36:20 And so I'm actually really excited about. Next category. The next category. Does the experience with cerebrus make you want to do a lot more? Investing in companies. No. But why not? In twenty nineteen, we're sitting in a board meeting. And this thing is melting.

36:37 It's like fucking melty. Okay, and we've raised five hundred million dollars or something. And it's like, wait, what? It's melting or burning or something in like we're looking. And I'm like, holy shit. I realize like five hundred million dollars isn't that much.

36:52 In today's era, but I was just like, We're gonna lose all this money. This is just not gonna work and What that team did. Insane. They're built differently. And I have so much respect and thanks to them.

37:03 for what they've done. There are efforts. That Make you really proud to be a venture capitalist. Because you're funding something that makes a difference and matters.

37:12 I'm an investor. Because that's a means to work with companies, not because I fundamentally love investing or something like that. I like working with the companies. That's my favorite part of it.

37:22 Working with Teams like that and companies like that is so special on these giant ambitious efforts. I said this well before twenty eighteen or whatever is Whether Srubus worked or didn't. I think it was an effort that was worth venture capital.

37:36 Those are kind of thing you should do. You should try to build. something that people have tried for fifty years and haven't been unable to, but now we think we could do and there's a reason and application for it and everything else. I love that. I do like those kinds of things, joking aside, and We do have a robotics company and then this CPU

37:51 project that we were talking about. I think these kinds of things are actually really fun. And interesting and good use of vendor capital. But they definitely aren't easy. The productive naivete that you described where It's probably a virtue that you didn't know more than you knew, otherwise you wouldn't have done it.

38:06 This is certainly my experience with actually like in any of these fields, you ask experts, they're gonna tell you don't do it. It sucks, it's too hard, base rate's too low. Young people can't do other than forty seven list. Yeah. Is there anywhere where that is just a bridge too far that could be like bio or something like this where You just

38:22 Are unwilling to invest. If you're naive. You know what's so funny is the hard thing about investing is Most of the time all of these stereotypical statements are correct. They're not correct.

38:33 Three times out of four. They're correct nineteen times out of twenty. Maybe ninety-nine times out of a hundred. Like they are correct. The thing is. Bruce, one of our founders always says what could go right. We have to ask ourselves, what could go right?

38:46 And do we see that path? Yeah. Young people can't build chips or you shouldn't do an O Semi company or you shouldn't do this or like whatever. All of that stuff is actually totally right. Except when it isn't. Apparently there's a saying that someone said to one of my partners, which was

39:00 If it doesn't work It'll be for all of the reasons that your partner said. If it does work. It will be because those reasons didn't matter. It's just such a good

39:11 example of this whole thing, which is yeah, most of the time when we lay out the reasons that a company won't work. It's right. But then sometimes they just don't matter. You and I both interested in Robotics.

39:22 It's not controversial that it robotics works. it might dwarf what we're currently living through. What do you think has to be true for it to work? Obviously it's exciting. I want a robot in my house following my laundry. It sounds great. It's kinda one of these classics. It's always ten years away. And it's been that way for a long time.

39:39 What do you see happening? What has to happen for this to actually be a thing in the near to medium term? We've had classic robotics forever and they're all over the place and they're on assembly lines and manufacturing lines and all those things. Where you're doing repetitive tasks in controlled environments. Repetitive task and controlled environments. is like more or less solved and like that you know, that'll continue to happen.

40:01 But having Tweaked tasks. In real world environments. That's where you need the AI. That's where you need the AI plus the robots. The trick with it all

40:11 Is You need a model. That can do that. Mm-hmm. Of course, the first problem is There is no internet.

40:19 Scale data. To bootstrap the whole thing. LMs were all bootstrapped on The internet. Which is a ton of human knowledge. And the equivalent for that.

40:28 For Robots doesn't exist. And people are trying different things with videos and simulations and teleoperation. So like there's a lot of different ways. But if you kinda think about take telop as an example, how much you need to tele op.

40:44 Robots. to get to an internet scale data. This first step is like getting A good set of data. to bootstrap the model. one of the I think insights that you can have is with the internet

40:58 There's like a lot of slope. Even before AI generated all this stuff, there was also a bunch of junk data. And some data was more valuable than others. Like you may value, okay, certain things on Reddit more valuable than other things. You might value Wikipedia more than Like other forums, you might value GitHub more than other things. And All of the model companies did that, right? They prioritiz data that was more valuable and less valuable and ran out through the model.

41:21 I think one of the most interesting things that these AI robotics companies are doing are saying, Okay, well let's just go after the high value data to start. If we go out for the high value data, then we can kind of bootstrap this model. Now once you do that. Then Can you through that pre training process get to a place

41:37 Where you can have Very small Of data. That

41:44 You add in post training. And all of a sudden it works for that. That's the magic we have with Lams is you have this giant pre trained And then You add a little magic on it.

41:55 in R L and post training. And you teach it a new thing that it wasn't in the pre-training set and you kinda go from there. And I think the exact same thing's happening in robotics. We're investors in Sunday Robotics, which is going after this exact pipe. It's a cool company and they're doing household robots. But the key part before you get to household and all that stuff matters much less actually. then can you get this training pipeline to work and how do you do that?

42:20 And one of the lessons I learned and I look back, I try to learn from history'cause it doesn't repeat but it rhymes and And if I look at autonomous vehicles as an example, which is They're robots, basically. They're AI plus robots.

42:34 You look at Waymo and you look at Tesla. They were both designed. Yeah, the Tesla Yeah, a fleet of Teslas that everybody owned. It was collecting data.

42:45 On the Teslas. And it was used to train the models to drive the Tusses. Same thing with Waymo. I think part of the lessons is they gather very, very high quality data. They did their pre-training and then they worked off of that.

42:58 That was a simplification. to allow you to get to A complete. product or complete solution, which of course is going to continue to improve and ultimately will generalize, I'm sure it'll generalize in some way. So you'll be able to strop it on any car and everything else.

43:13 So I think the same thing's happening in robotics where you have companies Like Sunday and others who are using techniques where They're vertically integrating the robot. And the model the data collection around the robot.

43:28 Sunday uses gloves. That are designed with the robot hands. So they're perfect. So you get very good data transferability from one to another. You do this pre training and you have a great pre training data set. You have a good model.

43:42 And then you start adding these examples and your RL and post training on top and you get cool emergent behavior. Do you have a most visceral moment? When we invested in Sunday the first time. We saw them.

43:54 partner Peter arranged. a demo we went down into the basement at the Stanford Lab and they had like this Totally janky cardboard glove thing. You see a few evolutions of it. The last time we saw a demo, we went down in the basement.

44:08 of their now office building. And there was like a dozen robots. just folding arbitrary line. It wasn't in the demo. It was just trial and error and then they have people like taking the clothes and then measuring them to make sure that they were folded properly and creating a rigorous baseline. an evaluation criteria.

44:26 And I was like Oh my God, this is happening. Do you ever worry about How to pick the right customer for these companies. Every one of these things folds laundry. Which like I don't think anyone likes folding laundry.

44:37 So it seems like a good use case, but It feels like we don't actually understand the demand. for what these things will be used to do. Do you ever worry about it That they were sort of building solutions that will then be in searches.

44:49 Of problems. I don't and I'll explain why and it's certainly informed by watching the L on evolution. Some of the people who were involved in the early L um at OpenAI really understood that code was gonna be important and obviously Yeah, and the Tropic team have this perspective that you could get to RSI if you've got code generation going and automating AI research and whatnot.

45:12 If you think about it, the first use cases were very much language oriented. They were very much essay writing and Editing and marketing like I think the first application that really took off was Jasper, which was just Writing marketing copy.

45:25 I think it's gonna evolve a lot, basically. I think laundry is kind of a good task because It's arbitrary, it's complex, it requires dexterous manipulation. It's it isn't time sensitive. If it takes three times longer, so be it. Who cares? It doesn't matter, just let it run all day. So I think it has like some of those properties. I don't think the task is actually that important.

45:45 What I think is much more important is Are you pre-training an amazing model? than being able to post train on top of it and get that flywheel going. If you get that flywheel going, then the task capability will just keep multiplying. Maybe this question will be annoying or slightly uncomfortable for you, but

46:03 If you ask Basically every founder And critically other investors of your type. Almost everyone If I ask like who's the best

46:11 Board partner? We'll say you. You come up way more often than anyone else that I've come across. And I'm curious why you think that is. What it is that you're doing that other people The incentives are there.

46:23 to do a great job as a board partner. What do you think you're doing? on the boards of these companies, partner with the founders. that's actually different from other really talented investors who are also nominally doing the same job, but don't come up nearly as often when asked that question. One of the things that I've realized is

46:40 We each are attracted to different types of entrepreneurs where we have chemistry. Coming. investor second and I try to be a partner first. We'll see companies come in. what I would call an investment great opportunity.

46:55 You can invest, it probably works. You make money. It's good. And an investor would do that. A partner wouldn't Because that's not sufficient for a partner.

47:05 Unless you have real chemistry with that person where you feel like you're gonna be able to work together really effectively. And I'm gonna learn a ton from them and they're gonna learn something from me. Together we're gonna just feed each other's loops. Unless you feel that way.

47:20 He can't be a partner. And so you pass on that. That's a really important like fit element to me. It kind of starts with this mutual selection, actually, weirdly. They want to partner.

47:31 with us and I wanna partner with them. I'm really like looking forward to working with them together. And I'll give you a really good example of where this comes into play for me. If I take sa gene and benchling,

47:41 Benching. Is life science and SAS. Companies absolutely crushed, done really, really well. And then of course you have this biotech crash. And everything else.

47:52 And the company. It became grimy. The company had never had any churn. For the longest time. Such that even on their reports for every SaaS company, you have this like okay, gross ARR added, churn line, net AR added, like everybody does the same thing.

48:07 They never had a turn line, never reported it. For the first of like six years that I worked with the company. So then they got seven years of churn in like twelve months. Turns out life sucks when you get seven years of churn and twelve months. Through that grind and through it all.

48:23 And related to the whole wait a minute, the goalpost moved. We have to do something different, everything else. they kept thinking about how to apply AI for these biotech and pharma customers, which they're very close to. How can we make it better for them? How can we apply these models in their world in a way that they're excited about and continue to iterate.

48:42 And it was grindy. And I was there for it. You're excited to work with that person. 'Cause like one, of course she thinks it's a really special opportunity and there's a way out. There's a path and we can find it. But two.

48:54 Because of the joy of the game, the relationship and like everything else. Like that's part of it. It's very different. I've seen different models and lots of different models of venture capital work. Muritz was a writer Dor was a sales guy. Girly was an engineer.

49:08 Peter's career venture capitalists, they're all different. working with them, it's like when I call these people, I learn something and they push back on me. And then I ask them questions. And what I've realized is like so much of my job Is they know the answer. They know what they want to do. They know the answer. And it's maybe asking questions.

49:27 of them To Maybe help. solidify their conviction or solidify their articulation. of what they want to do and why.

49:36 And you just keep doing that. Through that process, hopefully. We get One percent better. A few times a year we make a one percent better decision, two percent better decision a few times a year.

49:47 And if you do that over a decade. That compounds to real results. One of the questions that I ask myself Before making an investment is There are all these people that I care about.

49:57 through my life, like you carry yours. Could I Talk. One of them. And to go into this company.

50:05 And Honestly, intellectually, honestly, to my self. Explain to them why this could be their life's work. And if I can't do that, I should not invest. It just means that the project doesn't line up in that way. And so as long as we have one of those things.

50:20 It doesn't matter that much what it is to me. It's just it's important. It could make a big dent. And If it can make a big dent. And it's a special person I'd love to work on. Are there any other questions like that that you ask yourself before investing? That's a particularly good one.

50:35 The other question, if this person calls me at nine PM on a Saturday night, will I pick up the phone? That was at the green button test. Yeah, yeah, yeah. It's a chemistry thing. And they have to feel the same way. Obviously. One of the other ones. Is

50:47 If it's right. Does it matter? Which is different than this like first one, but it's there's so many things that we could be right on. As a business. I looked at one last week and I told her I was like, I really think that you can build an amazing company here and you just shouldn't raise venture capital.

51:02 But there's like so many things that you can be right on, but they ultimately just don't matter. Like nobody cares. That's a better way to say it. If we're right, we'll care. If they won't care, then you're just not gonna build enough equity value. That's another useful one. One of the coolest things that's happening right now is all of that that you just described.

51:19 has higher stakes and more leverage attached to it, which is manifested most simply in more dollars and higher prices. You and I have talked about this notion of what high multiple on invested capital investing is like and what it has been like and what it's moving into. You did something recently, which was you raised a growth fund for the first time in a long time. I think that is related to this concept. These companies need more capital, the prices are higher, the outcomes are bigger. Maybe we can earn the same multiple on a billion dollar entry price that we could on a fifty million dollar entry price ten years ago or whatever. Can you talk through that evolution, talk through the partnerships like way of thinking about it and talking about it, what you believe to be true.

51:56 That results in this decision to do this. Just go back to like why Did LP's starting with Swenson and everyone else, why do they start investing in venture capital? And fundamentally it wasn't because they thought they could beat the NASDAQ or the index by like three percentage points a year or five or whatever.

52:13 It's because there's situations where venture of capital could drive these insane multiples on investing capital. From a financial perspective, that's what they're seeking. For the longest time for most of the industry's history. Two things were synonymous.

52:27 early stage investing. and high cash on cash multiples. The way to get high cash on cash multiples was to do early stage. That's it. Those two circles in the Venn diagram like almost perfectly overlap. The thing that's changed is

52:42 recently, relatively recently. In the last few years. Because outcomes have gotten so much bigger and these markets are bigger and everything else. the circle of high cash on cash multiple opportunities is bigger than just early stage. And it's not.

52:58 So so big that there's a gazillion new companies in there that you can generate hundred X's on that's not true. But There's certainly Many.

53:07 outside of early stage. Where you can generate High returns. That's it. Think that's what we want to go after. You could argue we're a few years late. I think I'd take that criticism.

53:17 But I think that opportunity exists. On a go forward basis. We should go do it. everything else that we represent, which is the high conviction, high commitment. Partnership.

53:27 That has to still be there. As part of that discussion, what were like the other sides of the debate, such as Maybe we would have said the same thing in ninety nine and Halfway through twenty twenty. As markets get.

53:38 Exciting. the possibilities we all do this extrapolation error. What were the counter arguments to like let's Despite all that still not. I think the biggest counter argument that really made this time right versus two years ago or whatever. was you need the team that can do it. It's just a different mentality.

53:55 There are differences in how you evaluate and think about things. All the other stuff or there's you know, why not change and stay within your circle of competency and all those things are true. But that was like the biggest one. We had a f several examples over the last couple years. Where We

54:10 Had I think the right intuition on a company or an opportunity. We didn't do it. Because it was outside the Because it was outside the box. That's obviously dumb.

54:21 And I think it is quite different than a lot of Then she does we are really chasing these Very rare special companies that have like very high Cash and cash opportunities.

54:30 Where we think there's can be runaway successes and we can invest in them. Is there any lesson to be pulled from the many Let's call it twenty to one hundred Xs that you personally have observed. I mean it's Such a crazy amount of return. Obviously it doesn't pencil in the beginning. You can't make something pencil if it was that clear the price would be different. What have the twenty to one hundred X's taught you an aggregate, if anything.

54:53 Work with really special people. You want to work really hard. You want to work smart. And get lucky. And you need it all.

55:00 It really all has to come together. There's a lot of things. The are timing dependent. you have no control over as a company. And you take the Cerebus example as a good one, which is

55:11 This is our second we know, we took it public this time. In May, but we tried to take it public in twenty twenty four. And it would have been taken public at a much, much lower valuation and It didn't work out because of Cyphyus and all this stuff. So timing matters.

55:23 The advancement of that eighteen months made all the difference in the world for a bunch of things that were honestly outside of our control. There's some things that are in our control. getting inference running and everything else, but there was a lot of stuff that was outside of our control. These are all the classic things, we just gotta focus on what you can control. That's one of the Things that's really different than software companies. What software companies Aside from like building on AWS or whatever, you pretty much own your whole stack. And so you're really fully in control of your destiny in that way.

55:50 With hardware companies, you don't. There's an entire supply chain in HPM's HPM's a thing, D RAM's a thing and T S M C is a thing and a lot of those cross geopoliters. And so geopolitics gets involved and then that makes that complicated. That's a really big difference. And so you gotta get lucky on the timing in macro. And other stuff.

56:09 But I think it really starts with working with these crazy people with unbounded opportunities. And if you work with these crazy special people on unbounded opportunities You get lucky from time to time, you're bound to. When I was twenty years old. I was working on an investment bank.

56:24 Ben Orowitz, Markman had started Loud Cloud, it was still in stealth and Ben gave me an offer to be his assistant. I was talking to this associate who seemed like this elder of time, probably twenty five. He said this thing to me which stuck with me. He was like, Do you golf? It's like classic banking question, Do you golf? No, I don't fucking off. But he's like With golf.

56:41 You keep on practicing Keep getting the ball and a three part like close to the pen, close to the pen, close to the pen, close to the pen. You keep getting the ball close to the pen and you keep practicing That's hard work. That's like working smart. That's what you want to keep doing. Getting the hole in one?

56:56 That's luck. I kinda love that framing I use with my kids actually. What it says is like yeah, there's luck involved and there really is luck involved. But there is actually a way to increase your luck. And the way they increase your luck is get a lot of balls close to the pen.

57:10 Eventually one will drop. I'd like that. Each of us invests in one to two companies a year. I think in my twelve years I've invested in eighteen companies total. Crazy. Which is a relatively small number So it's a very high conviction and very high commitment.

57:23 I have a lot of skin in the game. I believe in these companies. If you keep on working with these very special people. And these opportunities Magic can happen.

57:33 What have you learned about the best? Reasons and conditions for going public. That does these Monday night dinners. And so we had a CEO last night. multi hundred billion dollar

57:44 private company and we had this whole conversation. It's just kind of fresh. I think that Ultimately When you go public. You have

57:54 A range of new opportunities and what you can do. Public trust. 'cause there's some transparency that comes with being public and being a public company. You obviously have a currency that you can do things with.

58:05 That ends up being there. Yeah, no. unbelievable ability to raise capital, which I think is why the labs will ultimately go. Although I think the trust thing is actually a really important element of why they should go and it's beneficial. to the world and to America if they do go public.

58:18 It's like Yeah, see what's going on. Everyone can see it. I think that's like a really beneficial setup. There's another element of it, which is what does a collegiate athlete want to do? GoPro. They want to play at a higher level. Is it harder? Yeah, it's harder.

58:32 Is the competition tougher? Yeah, the competition's tougher. They move faster. They're tougher, they're bigger, they're stronger. The stakes are bigger. The stage is bigger, the scrutiny's bigger. All of that's true.

58:43 It's kinda the same thing with companies. There are a handful and it really is a handful. Three, four, whatever. that can get to this tremendous scale without going public because Things have gone. Through their execution and excellence. They've lots of free cash flow and they've done really well over a really long time. And I think that's fantastic.

59:01 Good for them. In general for everyone else. Get out there. The other thing that I would tell you is There are windows.

59:10 for a particular type of company. the SaaS companies that went public in twenty twenty one. a whole boat of'em have struggled and it's been tough in the public markets'cause their stocks ripped to this multiple compression issue. They were training it thirty times. they've fourxed in size, but now they're trading it six times. It turns out you're under still

59:28 That's a tough place to be. I will also tell you that there's Five hundred Something. Probably Shao's companies that are

59:36 Between a hundred million and five hundred million and That are private. What happens? Those employees never got a chance to sell. Those employees don't have annual tenders. Those employees don't have an opportunity to exit. those investors don't have an opportunity to exit.

59:49 They're stuck. I don't think they're all going away and as I said, I don't think they're all getting vibe coded and everything else. But Ultimately The AI natives with their growth rates have sucked all the oxygen out of the room and all the interest.

1:00:02 And the window was missed. That's tough. What are the biggest debates right now inside of the partnership. I always love coming here and talking to you guys when there's something interesting going on because You to be

1:00:13 can be really fun to watch and I learn a lot from it. What are those debates today? There's a ton of debate around in this AI infra apps. for foundational models, infrared ecosystem. Where does value accrue?

1:00:28 And how does it accrue and where the moat and how do we think about that? But also the business model innovation. I think one of the things people don't understand. about why SaaS did so well versus traditional software was it wasn't just that it was a better delivery model and everything else. There was actual business model innovation on it. You really did have this subscription

1:00:48 element that ended up being fantastic. For both the company and the customers. It was a win win situation. That same thing actually exists in AI and Selling by outcome in that piece of it. But then wrapped up into that debate.

1:01:01 Discussion Is how much value just accrues. To the labs. Yes. How much of the value just accrues to the semis? That's a real discussion. What do you think?

1:01:11 I am of the view. That it all works. It's a very weird thing. Well the CSPs do well. Yes. Yes. Not all of'em, but will some of these neo cards do well? Yes.

1:01:22 Will the fireworks of the world do well? Yes. Will NVIDIA do well? Yes. Will these chip startups do well? Some set of'em. Yes.

1:01:32 Are we gonna have edge inference on our phones? Yes. Are we gonna have near edge infrares on Pops? Yes. Are we gonna have big models and data centers? Yes. There's so much zero sum thinking. Which is just like okay.

1:01:45 How do we cut up this pie and they're gonna eat this much and like oh no no Anthropic or whomever is gonna eat Ninety eight percent of the value and they're gonna do all the drug discovery, and I was like Come on. No. That's not what's gonna happen. When I say like I think everything's gonna work and I list it off all these every things, it's really important to understand that doesn't mean that every company that's doing every one of those things is gonna work. It actually

1:02:05 means quite the opposite of that. Most companies in each of those areas are not gonna work. And It's actually more important than ever to have real differentiation. To like really take each of these thoughts to their logical extreme.

1:02:17 and understand wait a minute, you gotta go all the way. on these things and really be differentiating on it. Is there anything you have your eye on? Whether it's in the funding market in the technology world. Anything at all that you really are watching carefully.

1:02:30 It's actually funny to me. Some of the people Are so so smart. And yeah. They're in this tech world where

1:02:39 They're like reaching these deterministic almost conclusions. Of like Mass unemployment and all of these different things. Yeah, I'm gonna give you a really concrete example, which I think is just so good. Take Jeff Hinton. and radiology.

1:02:52 So I think it was twenty sixteen where he was like, We should stop training radiologists. AI's gonna do it all better. Jeff Hintons. three orders of magnitude smarter than I am. Could not have been more wrong. But the actual

1:03:04 thing that led him to s make that statement or that conclusion was a hundred percent correct. If you look at these radiology images, we should be able to train AI to do a better job reading these things. Than humans. And that's probably true. And actually I think the studies and areas have shown that to be true.

1:03:21 And we have an investment in a company called New Lantern. Which is approaching us. But the big hurdle and the big thing that it articulated was like wait a minute, first off All of the aggregated training data set doesn't exist anywhere. What you see is companies going after like chest C Ts or like very specific elements, but

1:03:37 Your typical radiologist looks at a whole variety of things every single day from X rays to CTs to MRIs of all parts of the body and everything else. And so in AI climbing in specific areas like test CTs. is very marginally helpful. Because it's only doing that one thing, which could be one of twenty things or forty scans that they read that day. Problem number one, you don't have the data, just like we talked about in robotics and everything else. To train the I.

1:04:03 Problem number two. The whole Healthcare industry is oriented around reimbursing doctors for making readouts. How is that gonna work? And there's liability associated with that and there's repercussions.

1:04:14 of getting something wrong or missing something and there's medical malpractice and everything else. How are we going to avoid that? And how are we going to get around that? Problem number two. Real world stickiness. We're gonna end up with this application where AI really does help radiologists. It helps radiologists get more and more higher, higher throughput.

1:04:32 because they I can do some parts and the radiologist does some parts and they're checking each other and everything else. And you do kind of weirdly end up in this co pilot situation for some time. And then you're gonna slowly have the AI. Read more and more of the scans and build up and build up and build up. But the actual duration to get from here to there is gonna take a long time. In ensuing time

1:04:53 We need more radiologists, not less, because oh, by the way. Everyone's getting more imaging than they used to get. Because the cost of energy is going down in a Javant's paradox kind of way. My point on it is You have someone very, very smart who really understands the capabilities, really understands what's happening.

1:05:09 has the right data, but by not thinking of that data in the real world application comes to the wrong conclusion. And that's how I think of the unemployment thing. I think it's just it's almost the exact same setup. Eric, I love talking about markets and coaching with you. An absolute blast. Thanks for the time. Thank you. If you enjoyed this episode, visit Colossus.com. You'll find every episode of this podcast complete with hand edited transcripts. You can also subscribe to Colossus, our quarterly print, digital, and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most. Learn more at Colossus.com slash subscribe. You know how small advantages compound over time that's true in investing and just as true in how you run your company.

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